Feature detection and recognition systems such as face detection and recognition systems are utilized in a variety of security and identification applications. Feature detection and recognition systems typically receive data representing a video image of a face or other body part and determine data representing identification by comparing the video image to a library of data representative of faces or other body parts. It is desirable to use face recognition and detection systems to analyze video images from business meetings and video conference systems for security and other identification purposes. Video images from business meetings and video teleconference systems present a challenge to current commercially-available face detection and recognition systems.
Many conventional face detection and recognition systems are intended for higher quality, structured content, still images. For example, such systems often require a large video image of the face that is being identified and require that the face be imaged at a direct angle (i.e., an angle close to 90° with respect to the plane of the face). However, business meeting and video teleconference data tends to be unbounded, low-quality, continuous media which is difficult to use with commercially-available face detection and recognition systems. In these low-quality video environments, the faces in the video image are often extremely small (e.g., 20×20 pixels) and are rarely directed straight at the camera. Most commercial systems fail on such unstructured video images or scenes because they rely on the person looking straight at the video camera and require that each face be comprised of at least 80×80 pixels in the video frame.
FIG. 1 illustrates an example business meeting environment. FIG. 1 is a video image of a business environment which includes three participants sitting in various positions at a conference table and highlights some of the problems associated with using commercially available face recognition and detection systems with business meeting and video teleconference data. For example, a person 110 can have facial features obstructed from view by his or her hands. A person 120 can have a small face size viewed by the camera due to their distance from the camera. A person 130 can demonstrate a rotation of his or her head that takes the face in and out of a plane of rotation. Such obstructions, sizes, and unpredictable movements can be significant problems for feature based detection algorithms, such as face detection algorithms.
Thus, there is a need for a new feature detection method that combines color and motion filtering techniques with a post-processing filter that reduces the number of false detections produced by the overall system. Further, there is a need for an orientation invariant feature detection system and method for unstructured low-quality video. Even further, there is a need for a system and method for detection using low resolution imaging.
The teachings hereinbelow extend to those embodiments which fall within the scope of the appended claims, regardless of whether they accomplish one or more of the above-mentioned needs.